Evaluation of auto regressive integrated moving average (arima) and artificial neural networks (ann) in the prediction of effluent quality of a wastewater treatment system

Abstract

The main objective of wastewater treatment is to purify the water by degradation of organic matter in the water to anenvironmentally friendly status. To achieve this objective, some effluent (waste water) quality parameters such asChemical oxygen demand (COD) and Biochemical oxygen demand (BOD5) should be measured continuously in orderto meet up with the said objective and regulatory demands. However, through the prediction on water qualityparameters, effective guidance can be provided to comply with such demand without necessarily engaging in rigorouslaboratory analysis. Box-Jenkin’s Auto Regressive Integrated Moving Average (ARIMA) technique is one of the mostrefined extrapolation techniques for prediction while Artificial Neural Network (ANN) is a modern non-linear methodalso used for prediction. The Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root MeanSquare Error (RMSE) and Correlation coefficient (r) are used to evaluate the accuracy of the above-mentionedmodels. This paper examined the efficiency of ARIMA and ANN models in prediction of two major water qualityparameters (COD and BOD5) in a wastewater treatment plant. With the aid of R software, it was concluded that in allthe error estimates, ANNs models performed better than the ARIMA model, hence it can be used in the operation ofthe treatment system.